Step 1: Load packages

Step 2: Import raw data into R from Github

Infinite Warfare (2017)

champs2017 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2017-08-13-champs.csv"))

World War II (2018)

dallas2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2017-12-10-dallas.csv"))
neworleans2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-01-14-neworleans.csv"))
proleagueStageOne2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-04-08-proleague1.csv"))
atlanta2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-03-11-atlanta.csv"))
birmingham2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-04-01-birmingham.csv"))
proleagueRelegation2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-04-19-relegation.csv"))
seattle2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-04-22-seattle.csv"))
anaheim2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-06-17-anaheim.csv"))
proleagueStageTwo2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-07-29-proleague2.csv"))


# all stats for all major tournaments (EXCEPT CHAMPS) in WWII (2018)
allMajors2018 <- rbind(dallas2018, neworleans2018, proleagueStageOne2018, atlanta2018, birmingham2018, proleagueRelegation2018, seattle2018, anaheim2018, proleagueStageTwo2018)

# champs will act as our test data; we will try and predict the winner
champs2018 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2018-08-19-champs.csv"))

Black Ops 4 (2019)

proleagueQual2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-01-20-proleague-qual.csv"))
proleague2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-07-05-proleague.csv"))
fortworth2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-03-17-fortworth.csv"))
london2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-05-05-london.csv"))
anaheim2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-06-16-anaheim.csv"))
proleagueFinals2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-07-21-proleague-finals.csv"))

# all stats for all major tournaments (EXCEPT CHAMPS) in BO4 (2019)
allMajors2019 <- rbind(proleague2019, fortworth2019, london2019, anaheim2019, proleagueFinals2019)

# champs will act as our test data; we will try and predict the winner
champs2019 <- read_csv(url("https://raw.githubusercontent.com/Activision/cwl-data/master/data/data-2019-08-18-champs.csv"))

Step 3: Determine what data is valuable to us

What data are we concerned with? In order to assign an overall score to each individual player, we will need to address Hardpoint, Search and Destroy, and the 3rd Gamemode separately. Once we have an individual score for each of the three gamemodes, we can use these to determine a final score.

INFINITE WARFARE (2017)

Hardpoint: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. hill_time_s – hill time measured in seconds
8. hill_captures – shows activity on the map (MIGHT INCLUDE)
9. hill_defends – shows activity on the map (MIGHT INCLUDE)
10. multikills – x2_piece + x3_piece + x4_piece; multikills are often very influential in a match
11. match_id – helpful for getting rid of missing data

Search and Destroy: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. fb_round_ratio – ‘snd_firstbloods’/‘snd_rounds’ (NOT INCLUDED IN BASE DATA SET)
8. bomb_sneak_defuses – sneak defuses are often in pivotal rounds
9. bomb_plants – good indicator of role (MIGHT INCLUDE)
10. bomb_defuses – good indicator of role (MIGHT INCLUDE)
11. multikills – x2_piece + x3_piece + x4_piece; multikills are often very influential in a match
12. match_id – helpful for getting rid of missing data

Uplink: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. uplink_dunks – shows activity on the map (might include)
8. uplink_throws – shows activity on the map (might include)
9. uplink_points – shows activity on the map
10. multikills – x2_piece + x3_piece + x4_piece; multikills are often very influential in a match
11. match_id – helpful for getting rid of missing data

WWII (2018)

Hardpoint: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. hill_time_s – hill time measured in seconds
8. hill_captures – shows activity on the map (MIGHT INCLUDE)
9. hill_defends – shows activity on the map (MIGHT INCLUDE)
10. match_id – helpful for getting rid of missing data

Search and Destroy: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. fb_round_ratio – ‘snd_firstbloods’/‘snd_rounds’ (NOT INCLUDED IN BASE DATA SET)
8. bomb_sneak_defuses – sneak defuses are often in pivotal rounds
9. bomb_plants – good indicator of role (MIGHT INCLUDE)
10. bomb_defuses – good indicator of role (MIGHT INCLUDE)
11. match_id – helpful for getting rid of missing data

Capture the Flag: 1. player – what player does the data correspond to
2. mode – gamemode
3. win – ‘W’ or ‘L’; use to find overall player win/loss ratio
4. k_d – kill/death ratio; used to show overall impact on the map
5. assists – in addition to k/d, assists show overall support on the map; higher assists can indicate better team work
6. accuracy_percent – player accuracy for each match
7. ctf_captures – number of flags captured in the game; higher the better 8. ctf_returns – number of flags returned in the game; higher the better 9. ctf_pickups – number of flags picked up in the game; higher the better
10. match_id – helpful for getting rid of missing data

Black Ops 4 (2019)

EDA for INFINITE WARFARE: champs2017 data (only data for that year)

Organize data into work-able datasets

champs2017 <- champs2017 %>% clean_names(.)

# new dataset that contains all of the missing data, just in case
champs2017_missing <- sqldf('SELECT * FROM champs2017 WHERE match_id LIKE "missing%"')

# whole event data, all players and all maps, where player names are organized alphabetically
champs2017 <- champs2017[order(champs2017$player),]
# removes missing values
champs2017 <- sqldf('SELECT * FROM champs2017 WHERE match_id NOT LIKE "missing%"')
# calculates all the players that have played more than 20 games
playerNumGames_champs2017 <- count(champs2017, player) %>% subset(., n > 10) %>% remove_cols(n)

# final subset: includes all existing data for all players that have played more than 20 games (arbitrary number)
champs2017 <- sqldf('SELECT * FROM champs2017 WHERE player IN playerNumGames_champs2017')



# all player k_d card
allKD_champs2017 <- sqldf('SELECT player, k_d FROM champs2017')

# all necessary player data for Hardpoint
all_hp_champs2017 <- sqldf('SELECT player, win, k_d, assists, accuracy_percent, hill_time_s, hill_captures, hill_defends, multikills FROM champs2017 WHERE mode == "Hardpoint"')
all_hp_champs2017 <- all_hp_champs2017[order(all_hp_champs2017$player),]

# all necessary player data for Search and Destroy
all_snd_champs2017 <- sqldf('SELECT player, win, k_d, assists, accuracy_percent, bomb_sneak_defuses, bomb_plants, bomb_defuses, multikills, snd_rounds, snd_firstbloods FROM champs2017 WHERE mode == "Search & Destroy"')
all_snd_champs2017 <- all_snd_champs2017[order(all_snd_champs2017$player),]

# all necessary player data for Uplink
all_upl_champs2017 <- sqldf('SELECT player, win, k_d, assists, accuracy_percent, uplink_dunks, uplink_throws, uplink_points, multikills FROM champs2017 WHERE mode == "Uplink"')
all_upl_champs2017 <- all_upl_champs2017[order(all_upl_champs2017$player),]

EDA for WWII (2018): All season except champs

Organizing and cleaning raw data

# CLEANING
allMajors2018 <- allMajors2018 %>% clean_names(.)

# new dataset that contains all of the missing data, just in case
allMajors2018_missing <- sqldf('SELECT * FROM allMajors2018 WHERE match_id LIKE "missing%"')

# whole event data, all players and all maps, where player names are organized alphabetically
allMajors2018 <- allMajors2018[order(allMajors2018$player),]

# removes missing values
allMajors2018 <- sqldf('SELECT * FROM allMajors2018 WHERE match_id NOT LIKE "missing%"')

# calculates all the players that have played more than 20 games
playerNumGames <- count(allMajors2018, player) %>% subset(., n > 50) %>% remove_cols(n)

# final subset: includes all existing data for all players that have played more than 20 games (arbitrary number)
allMajors2018 <- sqldf('SELECT * FROM allMajors2018 WHERE player IN playerNumGames')





# all 2018 hardpoint data
all_hp_2018 <- sqldf('SELECT player, win, k_d, assists, accuracy_percent, hill_time_s, hill_captures, hill_defends FROM allMajors2018 WHERE mode == "Hardpoint"')
all_hp_2018 <- all_hp_2018[order(all_hp_2018$player),]





# all 2018 SND data
all_snd_2018 <- sqldf('SELECT player, match_id, win, k_d, assists, accuracy_percent, bomb_sneak_defuses, bomb_plants, bomb_defuses, snd_rounds, snd_firstbloods FROM allMajors2018 WHERE mode == "Search & Destroy"')

# adds new column with fb/round ratio
all_snd_2018 <- add_column(all_snd_2018, fb_round_ratio = all_snd_2018$snd_firstbloods/all_snd_2018$snd_rounds)

# adding a new column with average first bloods for the season
all_snd_2018 <- all_snd_2018 %>%
  group_by(player) %>%
  mutate(fb_avg = mean(k_d))

# puts data in alphabetical order
all_snd_2018 <- all_snd_2018[order(all_snd_2018$player),]





# all 2018 CTF data
all_ctf_2018 <- sqldf('SELECT player, win, k_d, assists, accuracy_percent, ctf_captures, ctf_returns, ctf_pickups FROM allMajors2018 WHERE mode == "Capture The Flag"')
all_ctf_2018 <- all_ctf_2018[order(all_ctf_2018$player),]

Some basic plots: KD for season

ggplot(allMajors2018, aes(x = reorder(player, k_d), y = k_d)) + geom_boxplot() + coord_flip(ylim = c(0, 3.5)) + labs(y = "Kill/death ratio", x = "Player", subtitle = "OVERALL Player K/D', 2018 Season (WWII), Descending")

ggplot(all_hp_2018, aes(x = reorder(player, k_d), y = k_d)) + geom_boxplot() + coord_flip(ylim = c(0, 3.5)) + labs(y = "Kill/death ratio", x = "Player", subtitle = "Player K/D's for HARDPOINT, 2018 Season (WWII), Descending")

ggplot(all_snd_2018, aes(x = reorder(player, k_d), y = k_d)) + geom_boxplot() + coord_flip(ylim = c(0, 5)) + labs(y = "Kill/death ratio", x = "Player", subtitle = "Player K/D's for SEARCH AND DESTROY, 2018 Season (WWII), Descending")

ggplot(all_ctf_2018, aes(x = reorder(player, k_d), y = k_d)) + geom_boxplot() + coord_flip(ylim = c(0, 3.5)) + labs(y = "Kill/death ratio", x = "Player", subtitle = "Player K/D's for CAPTURE THE FLAG, 2018 Season (WWII), Descending")

Some basic plots: Search and Destroy First Bloods

Search and Destroy is a gamemode that has multiple rounds, where in each round, every player only has one life. A “first blood” is the first kill of the round and is usually highly influential. This a common stat that commentators and the community look at.

ggplot(all_snd_2018, aes(x = reorder(player, fb_avg), y = fb_avg)) + geom_boxplot() + coord_flip(ylim = c(0, 3)) + labs(y = "Firstblood Average", x = "Player", subtitle = "Player Firstblood Average for SEARCH AND DESTROY, 2018 Season (WWII), Descending")

The top 5 players with the highest average firstbloods were:
1. Xotic
2. Aqua
3. Dashy
4. Slasher
5. Skrapz

ggplot(all_snd_2018, aes(x = reorder(player, snd_firstbloods), y = snd_firstbloods)) + geom_boxplot() + coord_flip(ylim = c(0, 8)) + labs(y = "Firstblood/round ratio", x = "Player", subtitle = "Player Firstbloods for SEARCH AND DESTROY, 2018 Season (WWII), Descending")

Firstbloods are incremental (by 1), which explains why all medians and outliers fall on an integer. The interesting part of this graph, to me, are the outliers. From looking at this plot, we can see that Blazt and Felony tied for most firstbloods in a game with 7 total firstbloods.

ggplot(all_snd_2018, aes(x = reorder(player, fb_round_ratio), y = fb_round_ratio)) + geom_boxplot() + coord_flip(ylim = c(0, 0.8)) + labs(y = "Firstblood/round ratio", x = "Player", subtitle = "Player Firstblood/Round for SEARCH AND DESTROY, 2018 Season (WWII), Descending")

The top 5 players with the highest firstblood/round ratio were:
1. Blazt
2. Fero
3. John
4. Zooma
5. Dashy